Analytic Hierarchy Process Sensitivity Research Articles

Assessment of effective parameters in landfill leachate treatment and optimization of the process using neural network, genetic algorithm and response surface methodology

Abstract In this paper, the Fenton process was found to be an effective technique to treat leachates when the drawbacks of this process were minimized by choosing proper catalyst, considering effective parameters and introducing optimum conditions. Analytic hierarchy process (AHP) was used to select the favorable catalyst between FeSO4 and FeCl2. Meanwhile, central composite design was used for test design of the experiments along with response surface methodology (RSM) and artificial neural network (ANN) for modeling. The effective variables included pH, [ H 2 O 2 ] / [ Fe 2+ ] , Fe 2+ dosage and initial COD concentration while removal COD, sludge to iron ration (SIR) and organic removal to sludge ratio (ORSR) were considered as targets. For all three targets, the effective factors were considered using sensitivity analysis. Finally, response surface methodology and genetic algorithm (GA) were used for optimization of the process. According to AHP sensitivity analysis results, priority percentage for FeCl2 and FeSO4 were 64% and 36%, respectively. Comparing RSM and ANN, it was found that ANN provided higher prediction ability while both models were statistically adequate (with R 2 > 0 .90 ) . The effective factors for COD removal, SIR and ORSR were respectively as follows: Fe 2+ dosage, pH and [ H 2 O 2 ] / [ Fe 2+ ] . The main focus of optimization was to achieve high removal efficiencies for COD when generated sludge is still low. Hence, results from RSM and GA indicated that both methods proposed approximately similar operational conditions. In the end, a practical formula was proposed for BOD5/COD ratio enhancement for each leachate.

Optimization of Fenton process using response surface methodology and analytic hierarchy process for landfill leachate treatment

Typically, to treat landfill leachate, biological techniques alone are not sufficient. In this study, Fenton process was found to be effective as a pretreatment method. Analytic hierarchy process (AHP) was used to select the favorable catalyst between FeSO4 and FeCl2 when three criteria, namely, (i) COD removal, (ii) sludge to iron ratio, and (iii) risk were considered. Meanwhile, response surface methodology was applied to model and optimize three target responses, (i) COD removal, (ii) sludge to iron ratio, and (iii) organic removal to sludge ratio. The effective variables included pH, [H2O2]/[Fe2+], Fe2+ dosage and reaction time. In addition, to minimize the danger, generated sludge was reused in the Fenton process. According to AHP sensitivity analysis results, priority percentage for FeCl2 and FeSO4 were 64% and 36%, respectively. Additionally, according to the statistical analysis, all suggested models were adequate (with R2 of 0.9171–0.9617). The optimum condition was found to be pH=6, [H2O2]/[Fe2+]=20moleratio, [Fe2+]=170mM, and reactiontime=105min. Results showed that [H2O2]/[Fe2+] and [Fe2+] are significant for COD removal while pH and [H2O2]/[Fe2+] were important factors for sludge to iron ratio (SIR) and organic removal to sludge ratio (ORSR), respectively. Meanwhile, when iron sludge was reused as catalyst, the results were acceptable even after five times regeneration.

Evaluating the Entry Mode for Taiwan Insurance Companies by Using AHP Sensitivity

Abstract This work presents an evaluation model that adopts several important criteria, enabling the Taiwan insurance industry to enter into the Chinese market in the most appropriate way. Major criteria weights are analyzed using an analytic hierarchy process (AHP) and sensitivity analysis. The results of the analysis indicate that the proposed evaluation model enables the Taiwan insurance industry to enter into the Chinese market more objectively by allowing companies to deploy effectively. Importantly, the proposed model can provide Taiwan’s insurance industry accreditation policy with reference material, making it highly applicable for academic and commercial purposes.

An organisational performance measurement model based on AHP sensitivity analysis

This research proposes an Analytic Hierarchy Process (AHP) sensitivity analysis to evaluate organisational performance of hospitals. The relative weights of evaluation criteria are determined using the AHP sensitivity analysis, followed by ranking of the hospitals for decision makers to evaluate in medical-related sectors. The AHP sensitivity analysis-based decision-making method is used to construct an evaluation method, which can provide decision makers or administrators of hospital with a valuable reference for evaluating the organisational performance. Consequently, the proposed model can assist the medical sector to assess the organisational performance of hospitals, making it highly applicable for academia and commercial purposes.

Using Treemaps to Visualize the Analytic Hierarchy Process

Treemaps, a visualization method for large hierarchical data spaces, are used to augment the capabilities of the Analytic Hierarchy Process (AHP) for decision-making. Two direct manipulation tools, presented metaphorically as a “pump” and a “hook,” were developed and applied to the treemap to support AHP sensitivity analysis. Users can change the importance of criteria dynamically on the two-dimensional treemap and immediately see the impact on the outcome of the decision. This fluid process dramatically speeds up exploration and provides a better understanding of the relative impact of the component criteria. A usability study with six subjects using a prototype AHP application showed that treemap representation was acceptable from a visualization and data operation standpoint.